Multimodal Sentiment Analysis with Adaptive Attention for Social Media Text and Images
Keywords:
multimodal sentiment analysis; adaptive attention; social media; vision-language models; fairness; governance; robustness; sociotechnical systemsAbstract
Social media platforms generate large volumes of multimodal content in which textual statements, images, emojis, and visual design elements jointly communicate sentiment. Models that treat these channels separately often fail to capture sarcasm, irony, affective contrast, and culturally embedded emotional cues. This paper presents a system-level examination of multimodal sentiment analysis that integrates adaptive attention mechanisms for aligning social media text and images. The discussion focuses on architectural design, fusion strategies, representation learning, governance, infrastructure deployment, robustness, fairness, and policy implications. Rather than proposing a single mathematical formulation, the paper analyzes structural trade-offs among early fusion, late fusion, tensor-based fusion, and cross-modal attention architectures. It considers how adaptive attention can regulate the influence of visual regions and textual tokens according to contextual relevance. The paper further examines how large-scale vision-language pretraining, parameter-efficient adaptation, and supervised contrastive objectives affect sentiment discrimination in noisy social media environments. Special attention is given to deployment concerns such as dataset documentation, demographic fairness, moderation use cases, computational sustainability, and resilience to distribution shift. The analysis suggests that adaptive attention is best understood as a sociotechnical design mechanism rather than an isolated model component. Its benefits depend on integration with clear governance, robust evaluation, and infrastructure choices that respect privacy, fairness, and operational constraints. The paper concludes by identifying directions for interdisciplinary research on multimodal sentiment systems that are both technically effective and institutionally accountable.
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